Clinical Decision Making
Notice bibliographique
Résumé
In Reply to Croskerry and Tait: We thank Drs. Croskerry and Tait for their interest in our report, which showed that there was a moderately strong inverse relationship between reading time and diagnostic accuracy. Croskerry and Tait interpret these results as “consistent with [the view that] intuitive decisions are more vulnerable to error than those made in the analytic system.” This conclusion derives from their interpretation that solution time reflects problem difficulty. Unfortunately, they miss a crucial point in the methodology: All subjects saw essentially the same cases. Thus, the inverse relation between time and accuracy reflects differences in participants, not cases. Further, in Table 2 we showed that this inverse relationship between time and accuracy holds for 23 of the 25 cases. We agree that there is no exact correspondence between length of time and reliance on System 1 or System 2 thinking. But if one accepts a strict dual-process theory, with a faster, contextual, nonanalytical System 1 and a slower, conceptual, analytical System 2, then faster times are likely a consequence of increased reliance on System 1 processes, and our conclusion—that “we found no support for the assertion that a longer time to diagnosis (typically associated with more deliberate, or System 2, processing) results in fewer errors”—follows directly. We recognize that reading a case is not the same as practice. But to assert without proof that “the exercises in the authors’ experiment do not reflect the actual complex processes involved in patient assessment” is too strong. We remind the authors that virtually all the evidence in support of dual-process theory, both in medicine and in the research program of Kahneman,1 derives from performance on written cases. Furthermore, many of the studies that have looked at the relationship between errors and System 1 and System 2 processes in real-world tasks and stimuli2–3 would support our conclusion that expert clinicians can use rapid and efficient heuristics to solve problems with fewer errors than when they use slower deliberative processes. We are puzzled by the authors’ statement that “faster responses … occur when the participants have the knowledge base to quickly arrive at the correct conclusion using System 2.” Our understanding is that System 1 thinking, based on knowledge related to prior experience, leads to rapid and (usually) correct solutions. Croskerry and Tait appear to assume three processes: slow and correct System 2, fast and error-prone System 1, and fast and correct System 2. This is clearly not what Kahneman,4 one of the founders of dual-processing theories, believes when he states that the operations of System 1 are fast, automatic, effortless, associative, and difficult to control or modify. The operations of System 2 are slower, serial, effortful, and deliberately controlled. Finally, we do not “promote the notion … that speed increases accuracy.” In our report, we stated, “as educators we should not encourage learners to speed up or avoid any reflection.” But we do want to stress that, even if one accepted a strict dual-process theory, System 1 thinking should not be blamed for all diagnostic errors. Jonathan Sherbino, MD Associate professor of emergency medicine, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada. Geoffrey R. Norman, PhD Professor of clinical epidemiology and biostatistics, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada; [email protected]. Wolfgang Gaissmaier, PhD Chief research scientist, Max Planck Institute for Human Development, Harding Center for Risk Literacy, Berlin, Germany.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,203 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,008 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,020 | 0,035 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,044 | 0,022 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».